Overview

Dataset statistics

Number of variables9
Number of observations1030
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)1.1%
Total size in memory72.6 KiB
Average record size in memory72.1 B

Variable types

Numeric9

Alerts

Dataset has 11 (1.1%) duplicate rowsDuplicates
Age is highly overall correlated with Concrete compressive strengthHigh correlation
Concrete compressive strength is highly overall correlated with AgeHigh correlation
Superplasticizer is highly overall correlated with WaterHigh correlation
Water is highly overall correlated with SuperplasticizerHigh correlation
Blast Furnace Slag has 466 (45.2%) zerosZeros
Fly Ash has 566 (55.0%) zerosZeros
Superplasticizer has 379 (36.8%) zerosZeros

Reproduction

Analysis started2023-12-05 12:32:25.745052
Analysis finished2023-12-05 12:32:32.257883
Duration6.51 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Cement
Real number (ℝ)

Distinct280
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.16563
Minimum102
Maximum540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-05T20:32:32.406593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile143.745
Q1192.375
median272.9
Q3350
95-th percentile480
Maximum540
Range438
Interquartile range (IQR)157.625

Descriptive statistics

Standard deviation104.50714
Coefficient of variation (CV)0.37169245
Kurtosis-0.52066328
Mean281.16563
Median Absolute Deviation (MAD)79.4
Skewness0.50951743
Sum289600.6
Variance10921.743
MonotonicityNot monotonic
2023-12-05T20:32:32.509072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
425 20
 
1.9%
362.6 20
 
1.9%
251.37 15
 
1.5%
446 14
 
1.4%
310 14
 
1.4%
331 13
 
1.3%
250 13
 
1.3%
475 13
 
1.3%
387 12
 
1.2%
349 12
 
1.2%
Other values (270) 884
85.8%
ValueCountFrequency (%)
102 4
0.4%
108.3 4
0.4%
116 4
0.4%
122.6 4
0.4%
132 2
 
0.2%
133 5
0.5%
133.1 1
 
0.1%
134.7 1
 
0.1%
135 2
 
0.2%
135.7 2
 
0.2%
ValueCountFrequency (%)
540 9
0.9%
531.3 5
0.5%
528 1
 
0.1%
525 7
0.7%
522 2
 
0.2%
520 2
 
0.2%
516 2
 
0.2%
505 1
 
0.1%
500.1 1
 
0.1%
500 10
1.0%

Blast Furnace Slag
Real number (ℝ)

ZEROS 

Distinct187
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.895485
Minimum0
Maximum359.4
Zeros466
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-05T20:32:32.606279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22
Q3142.95
95-th percentile236
Maximum359.4
Range359.4
Interquartile range (IQR)142.95

Descriptive statistics

Standard deviation86.279104
Coefficient of variation (CV)1.1675829
Kurtosis-0.5081392
Mean73.895485
Median Absolute Deviation (MAD)22
Skewness0.80073735
Sum76112.35
Variance7444.0837
MonotonicityNot monotonic
2023-12-05T20:32:32.713064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 466
45.2%
189 30
 
2.9%
106.3 20
 
1.9%
24 14
 
1.4%
20 12
 
1.2%
145 11
 
1.1%
19 10
 
1.0%
22 8
 
0.8%
26 8
 
0.8%
190 7
 
0.7%
Other values (177) 444
43.1%
ValueCountFrequency (%)
0 466
45.2%
0.02 5
 
0.5%
11 4
 
0.4%
13.61 5
 
0.5%
15 5
 
0.5%
17.2 1
 
0.1%
17.5 1
 
0.1%
17.6 1
 
0.1%
19 10
 
1.0%
20 12
 
1.2%
ValueCountFrequency (%)
359.4 2
 
0.2%
342.1 2
 
0.2%
316.1 2
 
0.2%
305.3 4
0.4%
290.2 2
 
0.2%
288 4
0.4%
282.8 4
0.4%
272.8 2
 
0.2%
262.2 5
0.5%
260 1
 
0.1%

Fly Ash
Real number (ℝ)

ZEROS 

Distinct163
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.187136
Minimum0
Maximum200.1
Zeros566
Zeros (%)55.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-05T20:32:32.816132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3118.27
95-th percentile167.0055
Maximum200.1
Range200.1
Interquartile range (IQR)118.27

Descriptive statistics

Standard deviation63.996469
Coefficient of variation (CV)1.181027
Kurtosis-1.3285048
Mean54.187136
Median Absolute Deviation (MAD)0
Skewness0.53744511
Sum55812.75
Variance4095.5481
MonotonicityNot monotonic
2023-12-05T20:32:32.924196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 566
55.0%
141 16
 
1.6%
118.27 15
 
1.5%
79 14
 
1.4%
94 13
 
1.3%
174.24 10
 
1.0%
98.75 10
 
1.0%
95.69 10
 
1.0%
125.18 10
 
1.0%
121.62 10
 
1.0%
Other values (153) 356
34.6%
ValueCountFrequency (%)
0 566
55.0%
24.46 5
 
0.5%
24.51 5
 
0.5%
24.52 5
 
0.5%
59 1
 
0.1%
60 1
 
0.1%
71 1
 
0.1%
71.5 1
 
0.1%
75.6 1
 
0.1%
76 1
 
0.1%
ValueCountFrequency (%)
200.1 1
 
0.1%
200 1
 
0.1%
195 3
0.3%
194.9 1
 
0.1%
194 1
 
0.1%
193 1
 
0.1%
190 1
 
0.1%
187 1
 
0.1%
185.3 1
 
0.1%
185 2
0.2%

Water
Real number (ℝ)

HIGH CORRELATION 

Distinct205
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.56636
Minimum121.75
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-05T20:32:33.030314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum121.75
5-th percentile146.14
Q1164.9
median185
Q3192
95-th percentile228
Maximum247
Range125.25
Interquartile range (IQR)27.1

Descriptive statistics

Standard deviation21.355567
Coefficient of variation (CV)0.11761852
Kurtosis0.12267634
Mean181.56636
Median Absolute Deviation (MAD)13
Skewness0.074323975
Sum187013.35
Variance456.06024
MonotonicityNot monotonic
2023-12-05T20:32:33.138155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192 118
 
11.5%
228 54
 
5.2%
185.7 46
 
4.5%
203.5 36
 
3.5%
186 28
 
2.7%
162 20
 
1.9%
164.9 20
 
1.9%
185 15
 
1.5%
153.5 15
 
1.5%
200 14
 
1.4%
Other values (195) 664
64.5%
ValueCountFrequency (%)
121.75 5
0.5%
126.6 5
0.5%
127 1
 
0.1%
127.3 1
 
0.1%
137.8 5
0.5%
140 1
 
0.1%
140.75 5
0.5%
141.8 5
0.5%
142 1
 
0.1%
143.3 5
0.5%
ValueCountFrequency (%)
247 1
 
0.1%
246.9 1
 
0.1%
237 1
 
0.1%
236.7 1
 
0.1%
228 54
5.2%
221.4 1
 
0.1%
221 2
 
0.2%
220.1 1
 
0.1%
220 2
 
0.2%
219.7 1
 
0.1%

Superplasticizer
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct155
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2031117
Minimum0
Maximum32.2
Zeros379
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-05T20:32:33.240579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.35
Q310.16
95-th percentile16.055
Maximum32.2
Range32.2
Interquartile range (IQR)10.16

Descriptive statistics

Standard deviation5.9734917
Coefficient of variation (CV)0.96298309
Kurtosis1.4131857
Mean6.2031117
Median Absolute Deviation (MAD)5.31
Skewness0.90811273
Sum6389.205
Variance35.682602
MonotonicityNot monotonic
2023-12-05T20:32:33.345795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 379
36.8%
8 27
 
2.6%
11.6 21
 
2.0%
7 19
 
1.8%
6 17
 
1.7%
9 15
 
1.5%
16.5 15
 
1.5%
10 15
 
1.5%
11 14
 
1.4%
5.75 10
 
1.0%
Other values (145) 498
48.3%
ValueCountFrequency (%)
0 379
36.8%
1.72 4
 
0.4%
1.9 1
 
0.1%
2 1
 
0.1%
2.2 1
 
0.1%
2.5 2
 
0.2%
3 6
 
0.6%
3.1 1
 
0.1%
3.4 3
 
0.3%
3.57 5
 
0.5%
ValueCountFrequency (%)
32.2 5
0.5%
28.2 5
0.5%
23.4 5
0.5%
22.1 1
 
0.1%
22 6
0.6%
20.8 1
 
0.1%
20 1
 
0.1%
19 1
 
0.1%
18.8 1
 
0.1%
18.6 5
0.5%

Coarse Aggregate
Real number (ℝ)

Distinct284
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean972.91859
Minimum801
Maximum1145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-05T20:32:33.448050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum801
5-th percentile842
Q1932
median968
Q31029.4
95-th percentile1104
Maximum1145
Range344
Interquartile range (IQR)97.4

Descriptive statistics

Standard deviation77.753818
Coefficient of variation (CV)0.079918113
Kurtosis-0.59900056
Mean972.91859
Median Absolute Deviation (MAD)46.3
Skewness-0.040206403
Sum1002106.2
Variance6045.6562
MonotonicityNot monotonic
2023-12-05T20:32:33.552618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
932 57
 
5.5%
852.1 45
 
4.4%
944.7 30
 
2.9%
968 29
 
2.8%
1125 24
 
2.3%
1047 19
 
1.8%
967 19
 
1.8%
974 12
 
1.2%
942 12
 
1.2%
938 12
 
1.2%
Other values (274) 771
74.9%
ValueCountFrequency (%)
801 4
0.4%
801.1 1
 
0.1%
801.4 1
 
0.1%
811 2
0.2%
814 1
 
0.1%
814.1 1
 
0.1%
817.9 1
 
0.1%
818 1
 
0.1%
819 2
0.2%
819.2 1
 
0.1%
ValueCountFrequency (%)
1145 1
 
0.1%
1134.3 5
 
0.5%
1130 1
 
0.1%
1125 24
2.3%
1124.4 2
 
0.2%
1120 2
 
0.2%
1119 2
 
0.2%
1118.8 2
 
0.2%
1118 1
 
0.1%
1113 2
 
0.2%

Fine Aggregate
Real number (ℝ)

Distinct304
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean773.57888
Minimum594
Maximum992.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-05T20:32:33.657304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum594
5-th percentile613
Q1730.95
median779.51
Q3824
95-th percentile898.068
Maximum992.6
Range398.6
Interquartile range (IQR)93.05

Descriptive statistics

Standard deviation80.175427
Coefficient of variation (CV)0.10364221
Kurtosis-0.10216477
Mean773.57888
Median Absolute Deviation (MAD)45.49
Skewness-0.2529793
Sum796786.25
Variance6428.0992
MonotonicityNot monotonic
2023-12-05T20:32:33.766062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
755.8 30
 
2.9%
594 30
 
2.9%
670 23
 
2.2%
613 22
 
2.1%
801 16
 
1.6%
746.6 15
 
1.5%
887.1 15
 
1.5%
845 14
 
1.4%
712 14
 
1.4%
750 12
 
1.2%
Other values (294) 839
81.5%
ValueCountFrequency (%)
594 30
2.9%
605 5
 
0.5%
611.8 5
 
0.5%
612 1
 
0.1%
613 22
2.1%
613.2 2
 
0.2%
614 1
 
0.1%
623 2
 
0.2%
630 5
 
0.5%
631 4
 
0.4%
ValueCountFrequency (%)
992.6 5
0.5%
945 4
0.4%
943.1 4
0.4%
942 4
0.4%
925.7 5
0.5%
905.9 5
0.5%
903.79 5
0.5%
903.59 5
0.5%
901.8 5
0.5%
900.9 5
0.5%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.662136
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-05T20:32:33.855326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median28
Q356
95-th percentile180
Maximum365
Range364
Interquartile range (IQR)49

Descriptive statistics

Standard deviation63.169912
Coefficient of variation (CV)1.38342
Kurtosis12.168989
Mean45.662136
Median Absolute Deviation (MAD)21
Skewness3.2691774
Sum47032
Variance3990.4377
MonotonicityNot monotonic
2023-12-05T20:32:33.934389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
28 425
41.3%
3 134
 
13.0%
7 126
 
12.2%
56 91
 
8.8%
14 62
 
6.0%
90 54
 
5.2%
100 52
 
5.0%
180 26
 
2.5%
91 22
 
2.1%
365 14
 
1.4%
Other values (4) 24
 
2.3%
ValueCountFrequency (%)
1 2
 
0.2%
3 134
 
13.0%
7 126
 
12.2%
14 62
 
6.0%
28 425
41.3%
56 91
 
8.8%
90 54
 
5.2%
91 22
 
2.1%
100 52
 
5.0%
120 3
 
0.3%
ValueCountFrequency (%)
365 14
 
1.4%
360 6
 
0.6%
270 13
 
1.3%
180 26
 
2.5%
120 3
 
0.3%
100 52
 
5.0%
91 22
 
2.1%
90 54
 
5.2%
56 91
 
8.8%
28 425
41.3%

Concrete compressive strength
Real number (ℝ)

HIGH CORRELATION 

Distinct938
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.817836
Minimum2.3318078
Maximum82.599225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-05T20:32:34.031560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.3318078
5-th percentile10.959428
Q123.707115
median34.442774
Q346.136287
95-th percentile66.804512
Maximum82.599225
Range80.267417
Interquartile range (IQR)22.429171

Descriptive statistics

Standard deviation16.705679
Coefficient of variation (CV)0.46640672
Kurtosis-0.31384369
Mean35.817836
Median Absolute Deviation (MAD)10.928195
Skewness0.41692228
Sum36892.371
Variance279.07972
MonotonicityNot monotonic
2023-12-05T20:32:34.138242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.39821744 5
 
0.5%
77.29715436 4
 
0.4%
31.35047372 4
 
0.4%
71.29871316 4
 
0.4%
35.3011712 4
 
0.4%
79.29663476 4
 
0.4%
55.89581932 3
 
0.3%
17.54026944 3
 
0.3%
18.12632404 3
 
0.3%
65.19685056 3
 
0.3%
Other values (928) 993
96.4%
ValueCountFrequency (%)
2.331807832 1
0.1%
3.31982694 1
0.1%
4.565020596 1
0.1%
4.782205536 1
0.1%
4.827710952 1
0.1%
4.903553312 1
0.1%
6.26733684 1
0.1%
6.280436884 1
0.1%
6.46728488 1
0.1%
6.8085755 1
0.1%
ValueCountFrequency (%)
82.5992248 1
 
0.1%
81.75116932 1
 
0.1%
80.19984832 1
 
0.1%
79.98611076 1
 
0.1%
79.40005616 1
 
0.1%
79.29663476 4
0.4%
78.80021204 1
 
0.1%
77.29715436 4
0.4%
76.80073164 1
 
0.1%
76.23536132 1
 
0.1%

Interactions

2023-12-05T20:32:31.397824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:25.813960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.481515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.151510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.840848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:28.669643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.332987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.064721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.753927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.466992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:25.877703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.551987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.220251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.906678image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:28.734703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.424691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.141871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.817613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.544170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:25.947824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.625572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.296710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.980746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:28.806742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.525512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.220622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.889798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.620640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.019399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.704063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.373825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:28.095574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:28.880822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.606761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.298671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.962235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.697575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.085625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.775589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.446301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:28.173827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:28.950120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.684847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.371700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.032667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.770631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.152805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.846627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.516865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:28.249969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.018647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.759015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.446462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.102210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.847349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.225949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.922494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.593853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:28.333838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.096931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.832308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.520497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.175663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.927170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.316617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.000796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.672287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:28.420897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.179845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.909534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.597364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.252567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:32.002642image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:26.404421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.073968image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:27.755056image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:28.591574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.255345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:29.986330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:30.673887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:31.322620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-05T20:32:34.214862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
AgeBlast Furnace SlagCementCoarse AggregateConcrete compressive strengthFine AggregateFly AshSuperplasticizerWater
Age1.000-0.0170.005-0.0450.596-0.0570.003-0.0100.091
Blast Furnace Slag-0.0171.000-0.250-0.3480.162-0.296-0.2470.0940.049
Cement0.005-0.2501.000-0.1450.478-0.174-0.4180.038-0.094
Coarse Aggregate-0.045-0.348-0.1451.000-0.184-0.1000.058-0.199-0.218
Concrete compressive strength0.5960.1620.478-0.1841.000-0.180-0.0780.348-0.308
Fine Aggregate-0.057-0.296-0.174-0.100-0.1801.0000.0510.168-0.346
Fly Ash0.003-0.247-0.4180.058-0.0780.0511.0000.455-0.283
Superplasticizer-0.0100.0940.038-0.1990.3480.1680.4551.000-0.687
Water0.0910.049-0.094-0.218-0.308-0.346-0.283-0.6871.000

Missing values

2023-12-05T20:32:32.104426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-05T20:32:32.211473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CementBlast Furnace SlagFly AshWaterSuperplasticizerCoarse AggregateFine AggregateAgeConcrete compressive strength
0540.00.00.0162.02.51040.0676.02879.986111
1540.00.00.0162.02.51055.0676.02861.887366
2332.5142.50.0228.00.0932.0594.027040.269535
3332.5142.50.0228.00.0932.0594.036541.052780
4198.6132.40.0192.00.0978.4825.536044.296075
5266.0114.00.0228.00.0932.0670.09047.029847
6380.095.00.0228.00.0932.0594.036543.698299
7380.095.00.0228.00.0932.0594.02836.447770
8266.0114.00.0228.00.0932.0670.02845.854291
9475.00.00.0228.00.0932.0594.02839.289790
CementBlast Furnace SlagFly AshWaterSuperplasticizerCoarse AggregateFine AggregateAgeConcrete compressive strength
1020288.4121.00.0177.47.0907.9829.52842.140084
1021298.20.0107.0209.711.1879.6744.22831.875165
1022264.5111.086.5195.55.9832.6790.42841.542308
1023159.8250.00.0168.412.21049.3688.22839.455954
1024166.0259.70.0183.212.7858.8826.82837.917043
1025276.4116.090.3179.68.9870.1768.32844.284354
1026322.20.0115.6196.010.4817.9813.42831.178794
1027148.5139.4108.6192.76.1892.4780.02823.696601
1028159.1186.70.0175.611.3989.6788.92832.768036
1029260.9100.578.3200.68.6864.5761.52832.401235

Duplicate rows

Most frequently occurring

CementBlast Furnace SlagFly AshWaterSuperplasticizerCoarse AggregateFine AggregateAgeConcrete compressive strength# duplicates
1362.6189.00.0164.911.6944.7755.8335.3011714
3362.6189.00.0164.911.6944.7755.82871.2987134
4362.6189.00.0164.911.6944.7755.85677.2971544
5362.6189.00.0164.911.6944.7755.89179.2966354
2362.6189.00.0164.911.6944.7755.8755.8958193
6425.0106.30.0153.516.5852.1887.1333.3982173
7425.0106.30.0153.516.5852.1887.1749.2010073
8425.0106.30.0153.516.5852.1887.12860.2946763
9425.0106.30.0153.516.5852.1887.15664.3005323
10425.0106.30.0153.516.5852.1887.19165.1968513